An adaptive estimation of periodic signals using a Fourier linear combiner

Vaz, Christopher A.; Kong, Xuan; Thakor, Nitish V.

doi:10.1109/78.258116

Cited by 98 publications

(60 citation statements)

References 13 publications

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“…This is possible by simply parallelizing the action of 5 filters, each of them tuned on a specific harmonic (ω c , 2ω c , ...5ω c ) as shown in figure 18. A similar approach is described in [24]. Figure 3 demonstrates the efficiency of this filter: the input and the output of the filter are drawn on the same plot.…”

Section: A Adaptive Disturbance Predictormentioning

confidence: 99%

Active filtering of physiological motion in robotized surgery using predictive control

et al. 2005

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Abstract-This paper presents a predictive control approach to active mechanical filtering of complex, periodic motions of organs induced by respiration or heart beating in robotized surgery. Two different predictive control schemes are proposed for the compensation of respiratory motions or cardiac motions.For respiratory motions, the periodic property of the disturbance has been included into the input-output model of the controlled system so as to have the robotic system learn and anticipate perturbation motions. A new cost function is proposed for the unconstrained generalized predictive controller (GPC) where reference tracking is decoupled from the rejection of predictable periodic motions.Cardiac motions are more complex since they are the combination of two periodic non-harmonic components. An adaptive disturbance predictor is proposed which outputs future predicted disturbance values. These predicted values are used to anticipate the disturbance by using the predictive feature of a regular GPC.Experimental results are presented on a laboratory testbed and in vivo on pigs. They demonstrate the effectiveness of the two proposed methods to compensate complex physiological motion.

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Section: A Adaptive Disturbance Predictormentioning

confidence: 99%

Active filtering of physiological motion in robotized surgery using predictive control

et al. 2005

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“…Some of the papers deal with stationary signals (in"nite adaptation time is available) with either random inputs [11,13,14,20,21], or deterministic inputs [4,46]. Other papers deal about slow time-varying signals where the signal dynamics is modeled with random walk [16,18,31,33,47] or Markov chains [32].…”

Section: The Lms Algorithm With Non-stationary Signalsmentioning

confidence: 99%

“…In [28] the reference inputs were the orthonormal Hermite functions, in [27,10] unit impulses, and in [42,46] …”

Section: Adaptive Coe7cient Estimation With the Lms Algorithmmentioning

confidence: 99%

ECG signal compression plus noise filtering with truncated orthogonal expansions

et al. 1999

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With the increasing use of the electrocardiographic signal (ECG) as a diagnostic tool in cardiology, there exists a requirement for e!ective ECG compression techniques. The goal of any data compression system is to maximize compression while minimizing distortion. Orthogonal expansions is a tool widely used because of its compression capacity in recurrent signals. In this paper we analyze the e!ect of noise in orthogonal expansions of ECG signals. When the observed signal is embedded in additive noise, distortion measurements, such as the mean-square error, are not a monotonic decreasing function of the number of transform coe$cients, due to the noise presence. We analyze and compare two di!erent ways to estimate the transform coe$cients: inner product and adaptive estimation with the LMS algorithm. For stationary signals, we demonstrate and quantify the superior performance obtained by the adaptive system when low values of the step-size are used ( . For non-stationary signals, we propose, based on experimental results, values of the LMS step-size depending on the noise characteristics and the signal-to-noise ratio. Theoretical results are contrasted with a simulation study with actual ECG signals from MIT-BIH Arrythmia database and three kinds of noise: simulated Gaussian white noise, and two records of physiological noise that essentially contains electrode motion artifacts and muscular activity.1999 Elsevier Science B.V. All rights reserved. Re2 sume2Avec l'usage croissant de signaux d'eH lectrocardiographie (ECG) comme outil diagnostique en cardiologie, il existe une demande pour des techniques de compression d'ECG e$caces. Le but de tout syste`me de compression de donneH es est de maximiser la compression tout en minimisant la distorsion. Les expansions orthogonales sont un outil largement utiliseH a`cause de ses capaciteH s de compression pour des signaux reH currents. Dans cet article, nous analysons l'e!et du bruit sur les expansions orthogonales de signaux ECG. Lorsque le signal observeH est inclus dans du bruit additif, des mesures de distorsion, telles l'erreur quadratique moyenne, ne sont pas une fonction monotone deH croissante du nombre de coe$cients transformeH s, a`cause de la preH sence du bruit. Nous analysons et comparons deux fac7 ons di!eH rentes d'estimer les coe$cients transformeH s: le produit interne et l'estimation adaptative avec l'algorithme LMS. Pour des signaux stationnaires, nous deH montrons et quanti"ons la performance supeH rieure obtenue par le syste`me adaptatif lorsque le pas utiliseH est petit. Pour des signaux non-stationnaires, nous proposons, sur la base de reH sultats expeH rimentaux, des valeurs pour le pas du LMS deH pendant des caracteH ristiques du bruit et du rapport signal sur bruit. Des reH sultats theH oriques sont compareH s avec des reH sultats de simulations faites avec de vrais signaux ECG de la base de donneH e d'arythmies MIT-BIH, pour trois types de bruits: un bruit blanc gaussien simuleH et deux enregistrements de bruit physiologisque qui contie...

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“…(27) Finally, the output of the filter to an impulse function located at sample m 0 for small values of ( 2 is neglected) can be calculated using (22) and (27) Remembering that the frequency response of the time-invariant system (comb filter) corresponding to the adaptive system with complete expansions is written in (16), and using (9), we can write …”

Section: Adaptive Estimation With the Lms Algorithmmentioning

confidence: 99%

Truncated orthogonal expansions of recurrent signals: equivalence to a linear time-variant periodic filter

Olmos

García

Jané

et al. 1999

IEEE Trans. Signal Process.

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Abstract-In this correspondence, we show that orthogonal expansions of recurrent signals like electrocardiograms (ECG's) with a reduced number of coefficients is equivalent to a linear time-variant periodic filter. Instantaneous impulse and frequency responses are analyzed for two classical ways of estimating the expansion coefficients: inner product and adaptive estimation with the LMS algorithm. The obtained description as a linear time-variant periodic filter is a useful tool in order to quantify the distortion produced by the effect of using a reduced number of coefficients in the expansion, and to give frequency criteria to select the appropriate number of functions. Moreover, the misadjustment of the LMS algorithm can be explained as a distortion of the instantaneous frequency response. Experimental results are illustrated with the Karhunen-Loeve transform of ECG signals, but this approach can also be applied to any orthogonal transform.Index Terms-Data compression, least mean squares methods, transforms. I. INTRODUCTIONOrthogonal expansion is a very well-known technique for signal analysis. It is based on the decomposition of the signal as a linear combination of simple and elementary basis functions [1]. An appropriate choice of the orthogonal basis functions achieves a signal representation, where each coefficient contributes with independent and complementary information, for example, frequency components for the Fourier transform, instantaneous signal values for the identity transform, localized frequency components using the wavelet transform, etc.When the same number of samples N in the signal under study is used as the number of basis functions in the expansion (exact modeling), the signal energy is completely represented, and the equivalent system can be considered to be the identity function. However, many applications (such as data compression [2]- [5], parameter extraction for pattern recognition, monitoring [6], etc.) require rank reduction. In these applications, the number of basis functions used is reduced to a fraction p < N (undermodeling), and accordingly, some signal components are discarded. The selection of the number of basis functions is the main problem because it is a tradeoff between signal representation capacity and rank reduction.In this correspondence, we show that the effect of using a reduced number of coefficients in orthogonal expansions of recurrent signals, like electrocardiograms (ECG), can be described as applying a linear time-variant periodic filter to the input signal. This equivalence gives a useful tool in order to quantify a priori in the frequency domain the distortion introduced in the reconstructed signal when a variable number of functions is used in the expansion. As a consequence, it can be used to establish a criteria to select the number of basis functions. Manuscript received January 22, 1998; revised May 6, 1999. This work was supported in part by TIC97-0945-CO2-01:02 from CICYT and P40/98 from CONSID-DGA. The associate editor coordinating the review o...

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An adaptive estimation of periodic signals using a Fourier linear combiner

Cited by 98 publications

References 13 publications

Active filtering of physiological motion in robotized surgery using predictive control

Active filtering of physiological motion in robotized surgery using predictive control

ECG signal compression plus noise filtering with truncated orthogonal expansions

Truncated orthogonal expansions of recurrent signals: equivalence to a linear time-variant periodic filter

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